So I have a dataset with columns that include name, age and a lot more of information on the players. I am pretty new at R and programming languages in general.So, I don't know how to write code which creates a function of which (1) the input is: player1,player (players which can be found in the column "name") and of which (2) the output is: their data on the columns: Name, age, overall, position and 6 different skill ratings(depending on the player's position on the field the output should be different(See the 6 data points needed for goalkeepers and the 6 data point needed for any other position in the script below). In a column named "Class" we can retrieve information about whether is a "Goalkeeper" or not. The rows in that column are filled with the variables "Attacker", "Midfielder", "Defender" and "Goalkeeper"
I wrote this:
Compare_Players <- Function(Player1, Player2) {
if ((Player1 == "Goalkeeper")) {
print(
Name,
Age,
Overall,
Position,
GKDiving,
GKHandling,
GKKicking,
GKPositioning,
GKReflexes,
Speed,
Skill.Moves
)
} else{
print(
Name,
Age,
Overall,
Position,
Pace,
Shooting,
Passing,
GeneralDribling,
Defending,
Physical,
Skill.Moves
)
}
What I want the function to look like in use: Compare_Players(Messi,Ronaldo)
Outcome: both their data on the above mentioned columns
#reprex (output of >dput(head(df1, 20))
structure(list(Name = c("L. Messi", "Cristiano Ronaldo", "Neymar Jr",
"De Gea", "K. De Bruyne", "E. Hazard", "L. Modrić", "L. Suárez",
"Sergio Ramos", "J. Oblak", "R. Lewandowski", "T. Kroos", "D. Godín",
"David Silva", "N. Kanté", "P. Dybala", "H. Kane", "A. Griezmann",
"M. ter Stegen", "T. Courtois"), Age = c(31L, 33L, 26L, 27L,
27L, 27L, 32L, 31L, 32L, 25L, 29L, 28L, 32L, 32L, 27L, 24L, 24L,
27L, 26L, 26L), Nationality = c("Argentina", "Portugal", "Brazil",
"Spain", "Belgium", "Belgium", "Croatia", "Uruguay", "Spain",
"Slovenia", "Poland", "Germany", "Uruguay", "Spain", "France",
"Argentina", "England", "France", "Germany", "Belgium"), Overall = c(94L,
94L, 92L, 91L, 91L, 91L, 91L, 91L, 91L, 90L, 90L, 90L, 90L, 90L,
89L, 89L, 89L, 89L, 89L, 89L), Potential = c(94L, 94L, 93L, 93L,
92L, 91L, 91L, 91L, 91L, 93L, 90L, 90L, 90L, 90L, 90L, 94L, 91L,
90L, 92L, 90L), Club = c("FC Barcelona", "Juventus", "Paris Saint-Germain",
"Manchester United", "Manchester City", "Chelsea", "Real Madrid",
"FC Barcelona", "Real Madrid", "Atlético Madrid", "FC Bayern München",
"Real Madrid", "Atlético Madrid", "Manchester City", "Chelsea",
"Juventus", "Tottenham Hotspur", "Atlético Madrid", "FC Barcelona",
"Real Madrid"), Value = c(110500000, 7.7e 07, 118500000, 7.2e 07,
1.02e 08, 9.3e 07, 6.7e 07, 8e 07, 5.1e 07, 6.8e 07, 7.7e 07,
76500000, 4.4e 07, 6e 07, 6.3e 07, 8.9e 07, 83500000, 7.8e 07,
5.8e 07, 53500000), Wage = c(565000, 405000, 290000, 260000,
355000, 340000, 420000, 455000, 380000, 94000, 205000, 355000,
125000, 285000, 225000, 205000, 205000, 145000, 240000, 240000
), Preferred.Foot = c("Left", "Right", "Right", "Right", "Right",
"Right", "Right", "Right", "Right", "Right", "Right", "Right",
"Right", "Left", "Right", "Left", "Right", "Left", "Right", "Left"
), International.Reputation = c(5, 5, 5, 4, 4, 4, 4, 5, 4, 3,
4, 4, 3, 4, 3, 3, 3, 4, 3, 4), Weak.Foot = c(4, 4, 5, 3, 5, 4,
4, 4, 3, 3, 4, 5, 3, 2, 3, 3, 4, 3, 4, 2), Skill.Moves = c(4,
5, 5, 1, 4, 4, 4, 3, 3, 1, 4, 3, 2, 4, 2, 4, 3, 4, 1, 1), Work.Rate = c("Medium/ Medium",
"High/ Low", "High/ Medium", "Medium/ Medium", "High/ High",
"High/ Medium", "High/ High", "High/ Medium", "High/ Medium",
"Medium/ Medium", "High/ Medium", "Medium/ Medium", "Medium/ High",
"High/ Medium", "Medium/ High", "High/ Medium", "High/ High",
"High/ High", "Medium/ Medium", "Medium/ Medium"), Body.Type = c("Messi",
"C. Ronaldo", "Neymar", "Lean", "Normal", "Normal", "Lean", "Normal",
"Normal", "Normal", "Normal", "Normal", "Lean", "Normal", "Lean",
"Normal", "Normal", "Lean", "Normal", "Courtois"), Position = c("RF",
"ST", "LW", "GK", "RCM", "LF", "RCM", "RS", "RCB", "GK", "ST",
"LCM", "CB", "LCM", "LDM", "LF", "ST", "CAM", "GK", "GK"), Jersey.Number = c(10,
7, 10, 1, 7, 10, 10, 9, 15, 1, 9, 8, 10, 21, 13, 21, 9, 7, 22,
1), Height = c(170, 188, 175, 193, 180, 173, 173, 183, 183, 188,
183, 183, 188, 173, 168, 178, 188, 175, 188, 198), Weight = c(72,
83, 68, 76, 70, 74, 66, 86, 82, 87, 80, 76, 78, 67, 72, 75, 89,
73, 85, 96), Crossing = c(84, 84, 79, 17, 93, 81, 86, 77, 66,
13, 62, 88, 55, 84, 68, 82, 75, 82, 15, 14), Finishing = c(95,
94, 87, 13, 82, 84, 72, 93, 60, 11, 91, 76, 42, 76, 65, 84, 94,
90, 14, 14), HeadingAccuracy = c(70, 89, 62, 21, 55, 61, 55,
77, 91, 15, 85, 54, 92, 54, 54, 68, 85, 84, 11, 13), ShortPassing = c(90,
81, 84, 50, 92, 89, 93, 82, 78, 29, 83, 92, 79, 93, 86, 87, 80,
83, 36, 33), Volleys = c(86, 87, 84, 13, 82, 80, 76, 88, 66,
13, 89, 82, 47, 82, 56, 88, 84, 87, 14, 12), Dribbling = c(97,
88, 96, 18, 86, 95, 90, 87, 63, 12, 85, 81, 53, 89, 79, 92, 80,
88, 17, 13), Curve = c(93, 81, 88, 21, 85, 83, 85, 86, 74, 13,
77, 86, 49, 82, 49, 88, 78, 84, 18, 19), FKAccuracy = c(94, 76,
87, 19, 83, 79, 78, 84, 72, 14, 86, 84, 51, 77, 49, 88, 68, 78,
12, 20), LongPassing = c(87, 77, 78, 51, 91, 83, 88, 64, 77,
26, 65, 93, 70, 87, 81, 75, 82, 76, 42, 35), BallControl = c(96,
94, 95, 42, 91, 94, 93, 90, 84, 16, 89, 90, 76, 94, 80, 92, 84,
90, 18, 23), Acceleration = c(91, 89, 94, 57, 78, 94, 80, 86,
76, 43, 77, 64, 68, 70, 82, 87, 68, 88, 38, 46), SprintSpeed = c(86,
91, 90, 58, 76, 88, 72, 75, 75, 60, 78, 62, 68, 64, 78, 83, 72,
85, 50, 52), Agility = c(91, 87, 96, 60, 79, 95, 93, 82, 78,
67, 78, 70, 58, 92, 82, 91, 71, 90, 37, 61), Reactions = c(95,
96, 94, 90, 91, 90, 90, 92, 85, 86, 90, 89, 85, 90, 93, 86, 91,
90, 85, 84), Balance = c(95, 70, 84, 43, 77, 94, 94, 83, 66,
49, 78, 71, 54, 90, 92, 85, 71, 80, 43, 45), ShotPower = c(85,
95, 80, 31, 91, 82, 79, 86, 79, 22, 88, 87, 67, 72, 71, 82, 88,
80, 22, 36), Jumping = c(68, 95, 61, 67, 63, 56, 68, 69, 93,
76, 84, 30, 91, 64, 77, 75, 78, 90, 79, 68), Stamina = c(72,
88, 81, 43, 90, 83, 89, 90, 84, 41, 78, 75, 66, 78, 96, 80, 89,
83, 35, 38), Strength = c(59, 79, 49, 64, 75, 66, 58, 83, 83,
78, 84, 73, 88, 52, 76, 65, 84, 62, 79, 70), LongShots = c(94,
93, 82, 12, 91, 80, 82, 85, 59, 12, 84, 92, 43, 75, 69, 88, 85,
82, 10, 17), Aggression = c(48, 63, 56, 38, 76, 54, 62, 87, 88,
34, 80, 60, 89, 57, 90, 48, 76, 69, 43, 23), Interceptions = c(22,
29, 36, 30, 61, 41, 83, 41, 90, 19, 39, 82, 88, 50, 92, 32, 35,
35, 22, 15), Positioning = c(94, 95, 89, 12, 87, 87, 79, 92,
60, 11, 91, 79, 48, 89, 71, 84, 93, 91, 11, 13), Vision = c(94,
82, 87, 68, 94, 89, 92, 84, 63, 70, 77, 86, 52, 92, 79, 87, 80,
83, 69, 44), Penalties = c(75, 85, 81, 40, 79, 86, 82, 85, 75,
11, 88, 73, 50, 75, 54, 86, 90, 79, 25, 27), Composure = c(96,
95, 94, 68, 88, 91, 84, 85, 82, 70, 86, 85, 82, 93, 85, 84, 89,
87, 69, 66), Marking = c(33, 28, 27, 15, 68, 34, 60, 62, 87,
27, 34, 72, 90, 59, 90, 23, 56, 59, 25, 20), StandingTackle = c(28,
31, 24, 21, 58, 27, 76, 45, 92, 12, 42, 79, 89, 53, 91, 20, 36,
47, 13, 18), SlidingTackle = c(26, 23, 33, 13, 51, 22, 73, 38,
91, 18, 19, 69, 89, 29, 85, 20, 38, 48, 10, 16), GKDiving = c(6,
7, 9, 90, 15, 11, 13, 27, 11, 86, 15, 10, 6, 6, 15, 5, 8, 14,
87, 85), GKHandling = c(11, 11, 9, 85, 13, 12, 9, 25, 8, 92,
6, 11, 8, 15, 12, 4, 10, 8, 85, 91), GKKicking = c(15, 15, 15,
87, 5, 6, 7, 31, 9, 78, 12, 13, 15, 7, 10, 4, 11, 14, 88, 72),
GKPositioning = c(14, 14, 15, 88, 10, 8, 14, 33, 7, 88, 8,
7, 5, 6, 7, 5, 14, 13, 85, 86), GKReflexes = c(8, 11, 11,
94, 13, 8, 9, 37, 11, 89, 10, 10, 15, 12, 10, 8, 11, 14,
90, 88), League = c("La Liga", "Serie A", "Ligue 1", "Premier League",
"Premier League", "Premier League", "La Liga", "La Liga",
"La Liga", "La Liga", "Bundesliga", "La Liga", "La Liga",
"Premier League", "Premier League", "Serie A", "Premier League",
"La Liga", "La Liga", "La Liga"), `Country/competition` = c("Spain",
"Italy", "France", "UK", "UK", "UK", "Spain", "Spain", "Spain",
"Spain", "Germany", "Spain", "Spain", "UK", "UK", "Italy",
"UK", "Spain", "Spain", "Spain"), Class = c("Forward", "Forward",
"Forward", "Goalkeeper", "Midfielder", "Forward", "Midfielder",
"Forward", "Defender", "Goalkeeper", "Forward", "Midfielder",
"Defender", "Midfielder", "Midfielder", "Forward", "Forward",
"Midfielder", "Goalkeeper", "Goalkeeper"), Pace = c(88, 90,
92, 58, 77, 91, 76, 80, 75, 52, 78, 63, 68, 67, 80, 85, 70,
86, 45, 49), Shooting = c(91, 93, 84, 18, 86, 83, 76, 89,
65, 14, 89, 82, 48, 76, 66, 85, 90, 86, 15, 20), Passing = c(90,
81, 83, 44, 92, 86, 90, 79, 72, 32, 75, 89, 65, 89, 77, 84,
79, 82, 37, 30), GeneralDribbling = c(96, 89, 95, 32, 87,
95, 92, 87, 72, 21, 85, 83, 62, 91, 80, 92, 80, 88, 21, 23
), Defending = c(32, 35, 32, 20, 61, 35, 70, 52, 90, 19,
41, 74, 89, 52, 87, 28, 47, 52, 18, 17), Physical = c(60,
79, 59, 54, 78, 67, 67, 85, 85, 60, 82, 69, 83, 60, 84, 66,
83, 70, 61, 52), Speed = c(89, 90, 92, 57, 77, 92, 77, 82,
76, 50, 77, 63, 68, 68, 80, 85, 70, 87, 43, 48)), row.names = c(NA,
20L), class = "data.frame")
CodePudding user response：
Here's another option:
library(tidyverse)
Compare_Players <- function(data, Player1, Player2){
stats <- c("Name", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "GeneralDribling", "Defending", "Physical", "Skill.Moves",
"GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
df %>%
filter(Name == !!enquo(Player1) | Name == !!enquo(Player2)) %>%
select(any_of(stats))
}
Compare_Players(df, "L. Messi", "De Gea")
This gives us:
Name Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed
1 L. Messi 31 94 RF 88 91 90 32 60 4 6 11 15 14 8 89
2 De Gea 27 91 GK 58 18 44 20 54 1 90 85 87 88 94 57
Edit:
If you want to see two players and different stats, you can combine into a list, but it's a bit more complicated:
Compare_Players <- function(data, Player1, Player2){
p1 <- data %>%
filter(Name == !!enquo(Player1))
p2 <- data %>%
filter(Name == !!enquo(Player2))
gk_stats <- c("Name", "Age", "Overall", "Position", "GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
stats <- c("Name", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "GeneralDribling", "Defending", "Physical", "Skill.Moves")
if (p1$Class == "Goalkeeper"){
p1 <- p1 %>% select(any_of(gk_stats))
} else {
p1 <- p1 %>% select(any_of(stats))
}
if (p2$Class == "Goalkeeper"){
p2 <- p2 %>% select(any_of(gk_stats))
} else {
p2 <- p2 %>% select(any_of(stats))
}
out <- list(p1,p2)
out
}
Which gives us:
> Compare_Players(df, "L. Messi", "De Gea")
[[1]]
Name Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves
1 L. Messi 31 94 RF 88 91 90 32 60 4
[[2]]
Name Age Overall Position GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed Skill.Moves
1 De Gea 27 91 GK 90 85 87 88 94 57 1
CodePudding user response：
Without a reprex it is gueswork as to what your data looks like. Assuming you have NA values for variables which are not applicable to players, as in this toy data set.
library(tidyverse)
#df <- tibble(Name = c("Messi", "Ronaldo", "RandomGuy"),
# Position = c("Somewhere", "Elsehwere", "Goalkeeper"),
# Pace = c(999, 555, NA),
# GKHandling = c(NA, NA, 15)
# )
df <- OP's reprex
Then we can use the nice rlang package to specify arbitary number of players to compare, tidy up the data using tidyr and subset the data using dplyr.
Compare_Players <- function(df, ...){
gk_stats <- c("First_name", "Surname", "Age", "Overall", "Position", "GKDiving", "GKHandling", "GKKicking", "GKPositioning", "GKReflexes", "Speed", "Skill.Moves")
stats <- c("First_name", "Surname", "Age", "Overall", "Position", "Pace", "Shooting", "Passing", "Defending", "Physical", "Skill.Moves")
players <- ensyms(...)
df %>%
extract(Name, into = c("First_name", "Surname"), "^(.* )(.*)") %>%
filter(Surname %in% !!players) %>%
select({if("GK" %in% .$Position) gk_stats else stats})
}
This allows us to write players without quotes directly in the function call.
For a detailed explanation of how this works, refer to [Advanced R][1]. Long story short - it takes arguments passed by the ..., and converts it such that the filter function knows it is a character vector.
We can now call Compare_Players(df, Messi, Ronaldo), which gives
# A tibble: 2 x 11
First_name Surname Age Overall Position Pace Shooting Passing Defending Physical Skill.Moves
<chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 L. Messi 31 94 RF 88 91 90 32 60 4
2 Cristiano Ronaldo 33 94 ST 90 93 81 35 79 5
Two caveats - if the player has a Surname that includes spaces, the last name segment is chosen. For this small set it is no issue, but if you have duplicates in the set you might want to apply additional filtering or fine tune the regex in the extract call. Finally, calling Compare_Players(df, Messi, Gea) returns
# A tibble: 2 x 12
First_name Surname Age Overall Position GKDiving GKHandling GKKicking GKPositioning GKReflexes Speed Skill.Moves
<chr> <chr> <int> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 L. Messi 31 94 RF 6 11 15 14 8 89 4
2 De Gea 27 91 GK 90 85 87 88 94 57 1
So beware of non-informative calls, as the function will return regardless [1]: https://adv-r.hadley.nz/quasiquotation.html